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Author(s):

Anisha Banu A | Deepa Mathi R | Nutheti Likhitha Chowdary
Keywords:

Brain Tumor Detection, Magnetic Resonance Imaging (MRI), Segmentation, Classification, Image Enhancement, Feature Extraction, Deep Learning, Transfer Learning, Quantum Machine Learning, Tumor Anatomy, Public Datasets, Tumor Variability, Future Trends, Lim
Abstract:
Brain Tumors Result From The Abnormal And Uncontrolled Growth Of Cells. If Left Untreated During The Early Stages, They Can Become Life-threatening. Although Numerous Significant Advancements Have Been Achieved In This Field, Ensuring Accurate Segmentation And Classification Remains A Complex Challenge. The Primary Difficulty In Detecting Brain Tumors Lies In The Variations In Their Location, Size, And Shape. This Paper Aims To Provide An Extensive Review Of Brain Tumor Detection Methods Using Magnetic Resonance Imaging (MRI) To Support Researchers In Their Work. It Encompasses Discussions On The Structure Of Brain Tumors, Publicly Accessible Datasets, Image Enhancement Techniques, Segmentation Methods, Feature Extraction, Classification Approaches, And The Role Of Advanced Technologies Such As Deep Learning, Transfer Learning, And Quantum Machine Learning In Analyzing Brain Tumors. Lastly, This Survey Summarizes Key Findings, Highlighting The Advantages, Limitations, Advancements, And Potential Future Directions In Brain Tumor Detection Research.